Joaquin Quiñonero Candela
Joaquin Quiñonero Candela is a Director of AI at Facebook. He built the AML (Applied Machine Learning) team, driving product impact at scale through applied research in machine learning, language understanding, computer vision, computational photography, augmented reality and other AI disciplines. AML also built the unified AI platform that powers all production applications of AI across the family of Facebook products. Joaquin is now focused on new AI challenges, including the ethics of AI.
Prior to Facebook, Joaquin built and taught a new machine learning course at the University of Cambridge, worked at Microsoft Research, and conducted postdoctoral research at three institutions in Germany, including the Max Planck Institute for Biological Cybernetics. He received his PhD from the Technical University of Denmark. Joaquin is a passionate runner (and new to triathlons), likes to play and sing folk songs on the guitar, and is devoted to his wife and three kids.
Emmanuel Jean Candès is the Barnum-Simons Chair in Mathematics and Statistics and Professor of Mathematics, of Statistics, and of Electrical Engineering at Stanford University, where he is also Director of Data Science. His research interests include compressive sensing, mathematical signal processing, computational harmonic analysis, statistics, and scientific computing. Candès is also interested in applications to the imaging sciences and inverse problems, as well as theoretical computer science, mathematical optimization, and information theory.
A graduate of the École Polytechnique, Candès earned a Ph.D. in statistics from Stanford University.
Candès has received numerous awards, including the James H. Wilkinson Prize in Numerical Analysis and Scientific Computing, the Vasil A. Popov Prize, the Alan T. Waterman Award, the George Pólya Prize (with Terence Tao), the ICIAM Collatz Prize, the Lagrange Prize in Continuous Optimization, the Dannie Heineman Prize, the George David Birkhoff Prize, and a MacArthur Fellowship. Candès is a fellow of SIAM and the American Mathematical Society, and was elected to the National Academy of Sciences.
Alexandra Chouldechova is an Assistant Professor of Statistics and Public Policy at Carnegie Mellon University's Heinz College of Information Systems and Public Policy. Her research focuses on questions of algorithmic fairness and accountability in data-driven decision making systems in criminal justice and human services. She is actively investigating how statistical methodologies can aid in developing decision making systems that mitigate biases and reduce disparities. In recent work she has studied the development and validation of criminal risk assessment tools in the presence of data bias, and affected community perspectives on the use of algorithmic decision making systems in child welfare.
Alexandra received a PhD in Statistics from Stanford University and a B.Sc. in Mathematical Statistics from the University of Toronto.
Jeff Dean (research.google.com/people/jeff) joined Google in 1999 and is currently a Google Senior Fellow and SVP, Research and Health. He and his collaborators are working on machine learning and AI techniques for speech recognition, computer vision, language understanding, robotics, healthcare and various other tasks. He is a co-designer and co-implementor of many important software systems, including MapReduce, BigTable Spanner systems, and the open-source TensorFlow (tensorflow.org) system for machine learning.
Jeff received a Ph.D. in Computer Science from the University of Washington in 1996 and a B.S. in computer science & economics from the University of Minnesota in 1990. He is a member of the U.S. National Academy of Engineering, and of the American Academy of Arts and Sciences, a Fellow of the Association for Computing Machinery (ACM), a Fellow of the American Association for the Advancement of Sciences (AAAS), and a winner of the ACM Prize in Computing and the Mark Weiser Award.
Andrew Gelman is a professor of statistics and political science at Columbia University. His books include Bayesian Data Analysis (with John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Don Rubin), Teaching Statistics: A Bag of Tricks (with Deb Nolan), Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do (with David Park, Boris Shor, and Jeronimo Cortina), A Quantitative Tour of the Social Sciences (co-edited with Jeronimo Cortina), and the forthcoming Regression and Other Stories (with Jennifer Hill and Aki Vehtari).
He has done research on a wide range of topics, including: why it is rational to vote; why campaign polls are so variable when elections are so predictable; reversals of death sentences; police stops in New York City, the statistical challenges of estimating small effects; the probability that your vote will be decisive; seats and votes in Congress; social network structure; arsenic in Bangladesh; radon in your basement; and pharmacology. He works in computational and graphical methods in statistics and is one of the developers of Stan, a probabilistic programming language for Bayesian inference.
Joseph Gonzalez is an assistant professor in the EECS department at University of California Berkeley and a founding member of the Berkeley RISE Lab where he studies the design of Real-time, Intelligent, Secure and Explainable systems. His research addresses problems in neural network design, efficient inference, computer vision, prediction serving, autonomous vehicles, machine learning lifecycle management, graph analytics, and distributed systems. Gonzalez also led the development of the Berkeley Data Science 100 course which is now taught to over 700 students a semester. Prior to joining Berkeley, Gonzalez co-founded Turi Inc (formerly GraphLab) based on his thesis work and created the GraphX project (now part of Apache Spark).
Shirley Ho is the group leader in Cosmology x Data science at Flatiron Institute in NYC. Ho's research interests have ranged from using machine learning and statistics to tackle fundamental challenges in cosmology to finding new structures in our own Milky Way. Ho has broad expertise in theoretical astrophysics, observational astronomy and data science. Ho’s recent interest has been on understanding and developing novel tools in machine learning techniques, and applying them to astrophysical challenges. Her goal is to understand the universe’s beginning, evolution and its ultimate fate.
Ho works with a wonderful group of international collaborators both within the Cosmology x Data Science Group at Flatiron Institute, at Department of Astrophysical Sciences at Princeton University, and beyond.
Yannis Ioannidis is a professor at the Department of Informatics and Telecommunications of the University of Athens, as well as the President and General Director of the “Athena” Research and Innovation Center. His research interests include database and information systems, personalization and social networks, data infrastructures and digital libraries & repositories, scientific systems and workflows, eHealth systems, and human-computer interaction, topics on which he has published over one hundred articles in leading journals and conferences. He also holds three patents. Yannis has been a (co-)principal investigator in over thirty five research projects funded by various government agencies and has been a member of the program committees of over sixty conferences, six times as (co-)chair. Yannis has served as the ACM SIGMOD Chair (July 2009-June 2013), following a 4-year term as Vice-Chair, and is or has been a member of several other executive bodies of professional organizations and Scientific Advisory Boards. In 2017, Professor Yannis E. Ioannidis received the 2017 ACM SIGMOD Contributions Award for his sustained leadership and dedicated service to the database community, especially as part of the SIGMOD Executive Committee and the VLDB Endowment.
Michael I. Jordan
Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley.
Jordan’s research interests bridge the computational, statistical, cognitive and biological sciences, and have focused in recent years on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines and applications to problems in distributed computing systems, natural language processing, signal processing and statistical genetics. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He is a Fellow of the American Association for the Advancement of Science. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the IJCAI Research Excellence Award in 2016, the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. He is a Fellow of the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA and SIAM.
Sham Kakade is a Washington Research Foundation Data Science Chair, with a joint appointment in the Department of Computer Science and the Department of Statistics at the University of Washington. He works on the theoretical foundations of machine learning, focusing on designing provable and practically efficient algorithms. Amongst his contributions, with a diverse set of collaborators, are: establishing principled approaches in reinforcement learning (including the natural policy gradient, conservative policy iteration, and the PAC-MDP framework); provably efficient tensor decomposition methods for estimation of latent variable models (including mixture of Gaussians, latent Dirichlet allocation, hidden markov models, and overlapping communities in social networks); optimal algorithms in the stochastic and non-stochastic multi-armed bandit problems (including the linear bandit and the Gaussian process bandit models); faster algorithms for large scale convex and nonconvex optimization (including how to escape from saddle points efficiently). He is the recipient of the IBM Goldberg best paper award (in 2007) for contributions to fast nearest neighbor search and the best paper, INFORMS Revenue Management and Pricing Section Prize (2014). He has been program chair for COLT 2011.
Sham completed his Ph.D. at the Gatsby Computational Neuroscience Unit at University College London, under the supervision of Peter Dayan, and he was a postdoc at the Dept. of Computer Science, University of Pennsylvania, under the supervision of Michael Kearns. Sham was an undergraduate at Caltech, studying in physics under the supervision of John Preskill. Sham has been a Principal Research Scientist at Microsoft Research, New England, an associate professor at the Department of Statistics, Wharton, UPenn, and an assistant professor at the Toyota Technological Institute at Chicago.
Daphne Koller is the CEO and Founder of insitro, a startup company that aims to rethink drug development using machine learning. She is also the Co-Chair of the Board and Co-Founder of Coursera, the largest platform for massive open online courses (MOOCs). Daphne was the Rajeev Motwani Professor of Computer Science at Stanford University, where she served on the faculty for 18 years. She has also been the Chief Computing Officer of Calico, an Alphabet company in the healthcare space. She is the author of over 200-refereed publications appearing in venues such as Science, Cell, and Nature Genetics. Daphne was recognized as one of TIME Magazine’s 100 most influential people in 2012 and Newsweek’s 10 most important people in 2010. She has been honored with multiple awards and fellowships during her career including the Sloan Foundation Faculty Fellowship in 1996, the ONR Young Investigator Award in 1998, the Presidential Early Career Award for Scientists and Engineers (PECASE) in 1999, the IJCAI Computers and Thought Award in 2001, the MacArthur Foundation Fellowship in 2004, and the ACM Prize in Computing in 2008. Daphne was inducted into the National Academy of Engineering in 2011 and elected a fellow of the American Academy of Arts and Sciences in 2014 and of the International Society of Computational Biology in 2017. Her teaching was recognized via the Stanford Medal for Excellence in Fostering Undergraduate Research, and as a Bass University Fellow in Undergraduate Education.
Kristian Lum is the Lead Statistician at the Human Rights Data Analysis Group (HRDAG), where she leads the HRDAG project on criminal justice in the United States. Previously, Kristian worked as a research assistant professor in the Virginia Bioinformatics Institute at Virginia Tech and as a data scientist at DataPad, a small technology start-up.
Kristian’s research primarily focuses on examining the uses of machine learning in the criminal justice system and has concretely demonstrated the potential for machine learning-based predictive policing models to reinforce and, in some cases, amplify historical racial biases in law enforcement. She has also applied a diverse set of methodologies to better understand the criminal justice system: causal inference methods to explore the causal impact of setting bail on the likelihood of pleading or being found guilty; and agent-based modeling methods derived from epidemiology to study the disease-like spread of incarceration through a social influence network. Additionally, Kristian’s work encompasses the development of new statistical methods that explicitly incorporate fairness considerations and advancing HRDAG’s core statistical methodology—record-linkage and capture-recapture methods for estimating the number of undocumented conflict casualties.
She is the primary author of the dga package, open source software for population estimation for the R computing environment.
Kristian received an MS and PhD from the Department of Statistical Science at Duke University and a BA in Mathematics and Statistics from Rice University.
David Madigan is a professor of statistics and dean emeritus of arts and science at Columbia University. He received a bachelor’s degree in Mathematical Sciences and a Ph.D. in Statistics, both from Trinity College Dublin. He has previously worked for AT&T Inc., Soliloquy Inc., the University of Washington, Rutgers University, and SkillSoft, Inc. He has over 200 publications in such areas as Bayesian statistics, text mining, Monte Carlo methods, pharmacovigilance and probabilistic graphical models. He is an elected Fellow of the American Association for the Advancement of Science, the American Statistical Association and of the Institute of Mathematical Statistics.
Aleksander Madry is the NBX Associate Professor of Computer Science in the MIT EECS Department and a principal investigator in the MIT CSAIL Laboratory. He received his PhD from MIT in 2011 and, prior to joining the MIT faculty, he spent some time at Microsoft Research New England and on the faculty of EPFL.
Aleksander's research interests span algorithms, continuous optimization, science of deep learning and understanding machine learning from a robustness perspective. His work has been recognized with a number of awards, including an NSF CAREER Award, an Alfred P. Sloan Research Fellowship, an ACM Doctoral Dissertation Award Honorable Mention, and the 2018 Presburger Award.
Xiao-Li Meng, the Whipple V. N. Jones Professor of Statistics, and the Founding Editor-in-Chief of Harvard Data Science Review, is well known for his depth and breadth in research, his innovation and passion in pedagogy, his vision and effectiveness in administration, as well as for his engaging and entertaining style as a speaker and writer. Meng was named the best statistician under the age of 40 by COPSS (Committee of Presidents of Statistical Societies) in 2001, and he is the recipient of numerous awards and honors for his more than 150 publications in at least a dozen theoretical and methodological areas, as well as in areas of pedagogy and professional development. He has delivered more than 400 research presentations and public speeches on these topics, and he is the author of “The XL-Files," a thought-provoking and entertaining column in the IMS (Institute of Mathematical Statistics) Bulletin. His interests range from the theoretical foundations of statistical inferences (e.g., the interplay among Bayesian, Fiducial, and frequentist perspectives; frameworks for multi-source, multi-phase and multi- resolution inferences) to statistical methods and computation (e.g., posterior predictive p-value; EM algorithm; Markov chain Monte Carlo; bridge and path sampling) to applications in natural, social, and medical sciences and engineering (e.g., complex statistical modeling in astronomy and astrophysics, assessing disparity in mental health services, and quantifying statistical information in genetic studies). Meng received his BS in mathematics from Fudan University in 1982 and his PhD in statistics from Harvard in 1990. He was on the faculty of the University of Chicago from 1991 to 2001 before returning to Harvard, where he served as the Chair of the Department of Statistics (2004-2012) and the Dean of Graduate School of Arts and Sciences (2012-2017).
Suchi Saria is the John C. Malone Assistant Professor at Johns Hopkins University where she directs the Machine Learning and Healthcare Lab. Her work with the lab enables new classes of diagnostic and treatment planning tools for healthcare—tools that use statistical machine learning techniques to tease out subtle information from “messy” observational datasets, and provide reliable inferences for individualizing care decisions.
Saria’s methodological work spans Bayesian and probabilistic approaches for addressing challenges associated with inference and prediction in complex, real-world temporal systems, with a focus in reliable ML, methods for counterfactual reasoning, and Bayesian nonparametrics for tackling sample heterogeneity and time-series data.
Her work has received recognition in numerous forms including best paper awards at machine learning, informatics, and medical venues, a Rambus Fellowship (2004-2010), an NSF Computing Innovation Fellowship (2011), selection by IEEE Intelligent Systems to Artificial Intelligence’s “10 to Watch” (2015), the DARPA Young Faculty Award (2016), MIT Technology Review’s ‘35 Innovators under 35’ (2017), the Sloan Research Fellowship in CS (2018), the World Economic Forum Young Global Leader (2018), and the National Academies of Medicine (NAM) Emerging Leader in Health and Medicine (2018). In 2017, her work was among four research contributions presented by Dr. France Córdova, Director of the National Science Foundation to Congress’ Commerce, Justice Science Appropriations Committee. Saria received her PhD from Stanford University working with Prof. Daphne Koller.
Richard J. Samworth
Professor Richard Samworth obtained his PhD in Statistics from the University of Cambridge in 2004, and has remained in Cambridge since, becoming a professor in 2013 and the Professor of Statistical Science in 2017. Richard is currently Director of the Statistical Laboratory in Cambridge and holds an Engineering and Physical Sciences fellowship. His main research interests are in high-dimensional and nonparametric statistics, including problems in data perturbation, changepoint estimation, shape-constrained inference and classification, amongst others. Richard currently serves as co-editor of the Annals of Statistics. He has received several honours and awards for his work, including most recently the COPSS Presidents' Award (2018), an Institute of Mathematical Statistics (IMS) Medallion lecture (2018) and the Adams prize (2017). He was elected a fellow of the American Statistical Association in 2015, a fellow of the IMS in 2014, and was awarded a Philip Leverhulme Prize in 2014 and the Royal Statistical Society Guy Medal in Bronze for 2012.
Ryan Tibshirani is an associate professor jointly appointed in the Departments of Statistics and Machine Learning at Carnegie Mellon University. He joined the Statistics faculty at Carnegie Mellon University in 2011 and the Machine Learning faculty in 2013. He earned his Ph.D. in Statistics at Stanford University. His thesis advisor was Jonathan Taylor.
Tibshirani's research interests lie broadly in statistics, machine learning, and optimization. More specifically, his interests include high-dimensional statistics, nonparametric regression, selective inference, distribution-free inference, graph-based learning, convex optimization, numerical methods, and implicit regularization. His main applied focus at this time is on methods for forecasting epidemics, primarily seasonal flu. He is the recipient of an NSF CAREER award, and his team has been named the CDC's most accurate flu forecaster for 4 years in a row.
Manuela M. Veloso recently joined J.P.Morgan Chase to create and head an Artificial Intelligence Research Center. Veloso is on leave from Carnegie Mellon University (CMU) where she is Herbert A. Simon University Professor in the School of Computer Science, and where she was the Head of the Machine Learning Department. She researches in AI, Robotics, and Machine Learning. At CMU, she founded and directs the CORAL research laboratory, for the study of autonomous agents that Collaborate, Observe, Reason, Act, and Learn. Veloso and her students research a variety of autonomous robots, including mobile service robots and soccer robots. Veloso is Fellow of the AAAI, AAAS, ACM, and IEEE. She is Einstein Chair Professor of the Chinese National Academy of Science, the co-founder and past President of RoboCup, and past President of AAAI. As of now, Professor Veloso has graduated 39 PhD students and co-authored more than 300 journal and conference publications. See www.cs.cmu.edu/~mmv for details.
Jeannette M. Wing is Avanessians Director of the Data Science Institute and Professor of Computer Science at Columbia University. She came to Columbia in July 2017 from Microsoft, where she served as Corporate Vice President of Microsoft Research, overseeing a global network of research labs. She is widely recognized for her intellectual leadership in computer science, particularly in trustworthy computing. Jeannette's seminal essay, titled “Computational Thinking,” was published more than a decade ago and is credited with helping to establish the centrality of computer science to problem-solving in fields where previously it had not been embraced.
Before joining Microsoft, Jeannette held positions at Carnegie Mellon University and at the National Science Foundation. She served Carnegie Mellon as Head of the Department of Computer Science and as Associate Dean for Academic Affairs of the School of Computer Science. At the National Science Foundation, she was Assistant Director of the Computer and Information Science and Engineering Directorate, where she oversaw the federal government’s funding of academic computer science research. Her areas of research expertise include security and privacy; formal methods; programming languages; and distributed and concurrent systems. Jeannette has been recognized with distinguished service awards from the Computing Research Association and the Association for Computing Machinery. She is a Fellow of the American Academy of Arts and Sciences, American Association for the Advancement of Science, the Association for Computing Machinery, and the Institute of Electrical and Electronic Engineers. She holds bachelor’s, master’s, and doctoral degrees from MIT.
Bin Yu is Chancellorâ€™s Professor in the Departments of Statistics and of Electrical Engineering & Computer Sciences at the University of California at Berkeley and a former chair of Statistics at UC Berkeley. Her research focuses on practice, algorithm, and theory of statistical machine learning and causal inference. Her group is engaged in interdisciplinary research with scientists from genomics, neuroscience, and precision medicine.
In order to augment empirical evidence for decision-making, they are investigating methods/algorithms (and associated statistical inference problems) such as dictionary learning, non-negative matrix factorization (NMF), EM and deep learning (CNNs and LSTMs), and heterogeneous effect estimation in randomized experiments (X-learner). Their recent algorithms include staNMF for unsupervised learning, iterative Random Forests (iRF) and signed iRF (s-iRF) for discovering predictive and stable high-order interactions in supervised learning, contextual decomposition (CD) and aggregated contextual decomposition (ACD) for phrase or patch importance extraction from an LSTM or a CNN.
Yu was a founding co-director of the Microsoft Research Asia (MSR) Lab at Peking Univeristy and is a member of the scientific advisory board at the Alan Turning Institute in the UK. She is a member of the U.S. National Academy of Sciences and Fellow of the American Academy of Arts and Sciences. She was a Guggenheim Fellow in 2006, and the Tukey Memorial Lecturer of the Bernoulli Society in 2012. She was President of IMS (Institute of Mathematical Statistics) in 2013-2014 and the Rietz Lecturer of IMS in 2016. She received the E. L. Scott Award from COPSS (Committee of Presidents of Statistical Societies) in 2018.